Self-Driving Car Engineer Nanodegree

Project: Vehicle Detection and Tracking**


Model Code

In [1]:
### Import the libraries

import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
import os
import time
from skimage.feature import hog
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

print("done")
done
In [2]:
### Build Images Variables

#Vehicle images
basedir = 'dataset/vehicles/vehicles/'
image_types = os.listdir(basedir)
cars = []
for imtype in image_types:
    cars.extend(glob.glob(basedir+imtype+'/*'))
    
print('Number of Vehicle Images found:', len(cars))
with open("cars.txt", 'w') as f:
    for fn in cars:
        f.write(fn+'\n')
        
#Non-vehicle images
basedir = 'dataset/non-vehicles/non-vehicles/'
image_types = os.listdir(basedir)
notcars = []
for imtype in image_types:
    notcars.extend(glob.glob(basedir+imtype+'/*'))
    
print('Number of Non-Vehicle Images found:', len(notcars))
with open("notcars.txt", 'w') as f:
    for fn in notcars:
        f.write(fn+'\n')
        
print("done")        
Number of Vehicle Images found: 8792
Number of Non-Vehicle Images found: 8968
done
In [3]:
### Define Functions

#def convert_color(img, conv='RGB2YCrCb'):
#    if conv == 'RGB2YCrCb':
#        return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
#    if conv == 'BGR2YCrCb':
#        return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
#    if conv == 'RGB2LUV':
#        return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
#
#

# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block, 
                        vis=False, feature_vec=True):
    # Call with two outputs if vis==True
    if vis == True:
        features, hog_image = hog(img, orientations=orient, 
                                  pixels_per_cell=(pix_per_cell, pix_per_cell),
                                  cells_per_block=(cell_per_block, cell_per_block), 
                                  transform_sqrt=True, 
                                  visualise=vis, feature_vector=feature_vec)
        return features, hog_image
    # Otherwise call with one output
    else:      
        features = hog(img, orientations=orient, 
                       pixels_per_cell=(pix_per_cell, pix_per_cell),
                       cells_per_block=(cell_per_block, cell_per_block), 
                       transform_sqrt=True, 
                       visualise=vis, feature_vector=feature_vec)
        return features

def bin_spatial(img, size=(32, 32)):
    color1 = cv2.resize(img[:,:,0], size).ravel()
    color2 = cv2.resize(img[:,:,1], size).ravel()
    color3 = cv2.resize(img[:,:,2], size).ravel()
    return np.hstack((color1, color2, color3))

## Define a function to compute binned color features  
#def bin_spatial(img, size=(32, 32)):
#    # Use cv2.resize().ravel() to create the feature vector
#    features = cv2.resize(img, size).ravel() 
#    # Return the feature vector
#    return features
    
def color_hist(img, nbins=32):    #bins_range=(0, 256)
    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:,:,0], bins=nbins)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins)
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
    # Return the individual histograms, bin_centers and feature vector
    return hist_features

## Define a function to compute color histogram features 
## NEED TO CHANGE bins_range if reading .png files with mpimg!
#def color_hist(img, nbins=32, bins_range=(0, 256)):
#    # Compute the histogram of the color channels separately
#    channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
#    channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
#    channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
#    # Concatenate the histograms into a single feature vector
#    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
#    # Return the individual histograms, bin_centers and feature vector
#    return hist_features

# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
                        hist_bins=32, orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,
                        spatial_feat=True, hist_feat=True, hog_feat=True):
    # Create a list to append feature vectors to
    features = []
    # Iterate through the list of images
    for file in imgs:
        file_features = []
        # Read in each one by one
        image = mpimg.imread(file)
        # apply color conversion if other than 'RGB'
        if color_space != 'RGB':
            if color_space == 'HSV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
            elif color_space == 'LUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
            elif color_space == 'HLS':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
            elif color_space == 'YUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
            elif color_space == 'YCrCb':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
        else: feature_image = np.copy(image)      

        if spatial_feat == True:
            spatial_features = bin_spatial(feature_image, size=spatial_size)
            file_features.append(spatial_features)
        if hist_feat == True:
            # Apply color_hist()
            hist_features = color_hist(feature_image, nbins=hist_bins)
            file_features.append(hist_features)
        if hog_feat == True:
        # Call get_hog_features() with vis=False, feature_vec=True
            if hog_channel == 'ALL':
                hog_features = []
                for channel in range(feature_image.shape[2]):
                    hog_features.append(get_hog_features(feature_image[:,:,channel], 
                                        orient, pix_per_cell, cell_per_block, 
                                        vis=False, feature_vec=True))
                hog_features = np.ravel(hog_features)        
            else:
                hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                            pix_per_cell, cell_per_block, vis=False, feature_vec=True)
            # Append the new feature vector to the features list
            file_features.append(hog_features)
        features.append(np.concatenate(file_features))
    # Return list of feature vectors
    return features
    
# Define a function that takes an image,
# start and stop positions in both x and y, 
# window size (x and y dimensions),  
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None], 
                    xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
    # If x and/or y start/stop positions not defined, set to image size
    if x_start_stop[0] == None:
        x_start_stop[0] = 0
    if x_start_stop[1] == None:
        x_start_stop[1] = img.shape[1]
    if y_start_stop[0] == None:
        y_start_stop[0] = 0
    if y_start_stop[1] == None:
        y_start_stop[1] = img.shape[0]
    # Compute the span of the region to be searched    
    xspan = x_start_stop[1] - x_start_stop[0]
    yspan = y_start_stop[1] - y_start_stop[0]
    # Compute the number of pixels per step in x/y
    nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
    ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
    # Compute the number of windows in x/y
    nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
    ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
    nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step) 
    ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step) 
    # Initialize a list to append window positions to
    window_list = []
    # Loop through finding x and y window positions
    # Note: you could vectorize this step, but in practice
    # you'll be considering windows one by one with your
    # classifier, so looping makes sense
    for ys in range(ny_windows):
        for xs in range(nx_windows):
            # Calculate window position
            startx = xs*nx_pix_per_step + x_start_stop[0]
            endx = startx + xy_window[0]
            starty = ys*ny_pix_per_step + y_start_stop[0]
            endy = starty + xy_window[1]
            
            # Append window position to list
            window_list.append(((startx, starty), (endx, endy)))
    # Return the list of windows
    return window_list

# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # Make a copy of the image
    imcopy = np.copy(img)
    # Iterate through the bounding boxes
    for bbox in bboxes:
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    # Return the image copy with boxes drawn
    return imcopy

# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
                        hist_bins=32, orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,
                        spatial_feat=True, hist_feat=True, hog_feat=True, vis=False):    
    #1) Define an empty list to receive features
    img_features = []
    #2) Apply color conversion if other than 'RGB'
    if color_space != 'RGB':
        if color_space == 'HSV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
        elif color_space == 'LUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
        elif color_space == 'HLS':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
        elif color_space == 'YUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
        elif color_space == 'YCrCb':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    else: feature_image = np.copy(img)      
    #3) Compute spatial features if flag is set
    if spatial_feat == True:
        spatial_features = bin_spatial(feature_image, size=spatial_size)
        #4) Append features to list
        img_features.append(spatial_features)
    #5) Compute histogram features if flag is set
    if hist_feat == True:
        hist_features = color_hist(feature_image, nbins=hist_bins)
        #6) Append features to list
        img_features.append(hist_features)
    #7) Compute HOG features if flag is set
    if hog_feat == True:
        if hog_channel == 'ALL':
            hog_features = []
            for channel in range(feature_image.shape[2]):
                hog_features.extend(get_hog_features(feature_image[:,:,channel], 
                                    orient, pix_per_cell, cell_per_block, 
                                    vis=False, feature_vec=True))      
        else:
            if vis == True:
                hog_features, hog_image = get_hog_features(feature_image[:,:,hog_channel], orient, 
                            pix_per_cell, cell_per_block, vis=True, feature_vec=True)
            else:
                hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                            pix_per_cell, cell_per_block, vis=False, feature_vec=True)
                
        #8) Append features to list
        img_features.append(hog_features)

    #9) Return concatenated array of features
    if vis == True:
        return np.concatenate(img_features), hog_image
    else:
        return np.concatenate(img_features)

# Define a function you will pass an image 
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB', 
                    spatial_size=(32, 32), hist_bins=32, 
                    hist_range=(0, 256), orient=9, 
                    pix_per_cell=8, cell_per_block=2, 
                    hog_channel=0, spatial_feat=True, 
                    hist_feat=True, hog_feat=True):

    #1) Create an empty list to receive positive detection windows
    on_windows = []
    #2) Iterate over all windows in the list
    for window in windows:
        #3) Extract the test window from original image
        test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))      
        #4) Extract features for that window using single_img_features()
        features = single_img_features(test_img, color_space=color_space, 
                            spatial_size=spatial_size, hist_bins=hist_bins, 
                            orient=orient, pix_per_cell=pix_per_cell, 
                            cell_per_block=cell_per_block, 
                            hog_channel=hog_channel, spatial_feat=spatial_feat, 
                            hist_feat=hist_feat, hog_feat=hog_feat)
        #5) Scale extracted features to be fed to classifier
        test_features = scaler.transform(np.array(features).reshape(1, -1))
        #6) Predict using your classifier
        prediction = clf.predict(test_features)
        #7) If positive (prediction == 1) then save the window
        if prediction == 1:
            on_windows.append(window)
    #8) Return windows for positive detections
    return on_windows

#plotting multiple images - based on the tutorial
def visualize(fig, rows, cols, imgs, titles):
    for i, img in enumerate(imgs):
        plt.subplot(rows, cols, i+1)
        plt.title(i+1)
        img_dims = len(img.shape)
        if img_dims < 3:
            plt.imshow(img, cmap='hot')
            plt.title(titles[i])
            #plt.imsave('output_images/'+titles[i]+'_test.png',img*255)
            #plt.imsave('output_images/'+titles[i]+'_test.png',cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            #plt.imsave('output_images/'+'title'+'_test.png',car_hog_image, cmap='gray')
            #cv2.imwrite('/output_images/' + titles[i] + '.png',img)
        else:
            plt.imshow(img)
            plt.title(titles[i])
            #plt.imsave('output_images/'+titles[i]+'_test.png',img*255)
            #plt.imsave('output_images/'+titles[i]+'_test.png',cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            #plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            #cv2.imwrite('output_images/'+titles[i]+'_test.png',img*255)
            #cv2.imwrite('/output_images/' + titles[i] + '.png',img)            

print("done")
done
In [4]:
# Use HOG on test images

%matplotlib inline

#random indices selection
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))

#read images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])

#tweak these parameters and see how the results change.
color_space = 'RGB' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
#orient = 9  # HOG orientations
orient = 6  # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 0 # Can be 0, 1, 2, or "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 16    # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off

car_features, car_hog_image = single_img_features(car_image, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
notcar_features, notcar_hog_image = single_img_features(notcar_image, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat, vis=True)

images = [car_image, car_hog_image, notcar_image, notcar_hog_image]
titles = ['car image', 'car HOG image', 'notcar_image', 'notcar HOG image']
fig = plt.figure(figsize=(12,3))
visualize(fig, 1, 4, images, titles)
print("done")

for i, img in enumerate(images):
    plt.imsave('output_images/'+titles[i]+'_test.png',images[i]*255)
done
In [5]:
# Train the Model - only on a subset of samples

#tweak these parameters and see how the results change.
#color_space = 'RGB' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
#orient = 6  # HOG orientations
orient = 9  # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
#hog_channel = 0 # Can be 0, 1, 2, or "ALL"
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
#spatial_size = (16, 16) # Spatial binning dimensions
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 16    # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
#y_start_stop = [None, None] # Min and max in y to search in slide_window()
#y_start_stop = [400,656]
#y_start_stop = [400,724]
#y_start_stop = [654,656] 

t=time.time()
n_samples = 1000
random_idxs = np.random.randint(0,len(cars), n_samples)
test_cars = cars
test_notcars = notcars
#test_cars = np.array(cars)[random_idxs]
#test_notcars = np.array(notcars)[random_idxs]

car_features = extract_features(test_cars, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(test_notcars, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)

print(time.time()-t, 'Seconds to compute features...')

X = np.vstack((car_features, notcar_features)).astype(np.float64)                        
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)

# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))

# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
#X_train, X_test, y_train, y_test = train_test_split(
#    scaled_X, y, test_size=0.2, random_state=rand_state)
X_train, X_test, y_train, y_test = train_test_split(
    scaled_X, y, test_size=0.1, random_state=rand_state)

print('Using:',orient,'orientations',pix_per_cell,
    'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC 
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
113.65391516685486 Seconds to compute features...
Using: 9 orientations 8 pixels per cell and 2 cells per block
Feature vector length: 8412
58.36 Seconds to train SVC...
Test Accuracy of SVC =  0.9932
In [6]:
# Perform a test on images to see bounding boxes

#%matplotlib inline
searchpath = 'test_images/*'  
example_images = glob.glob(searchpath)
#searchpath = 'test_images/'
#example_images = glob.glob(searchpath+'/*')
images = []
titles = []
#y_start_stop = [None, None] # Min and max in y to search in slide_window()
y_start_stop = [400,656]
overlap = 0.5
#xy_window = 64
xy_window = 96
#xy_window = 128

for img_src in example_images:
    t1 = time.time()
    img = mpimg.imread(img_src)
    draw_img = np.copy(img)
    img = img.astype(np.float32)/255
    print(np.min(img), np.max(img))
    
    windows = slide_window(img, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                        xy_window=(xy_window, xy_window), xy_overlap=(overlap, overlap))

    hot_windows = search_windows(img, windows, svc, X_scaler, color_space=color_space, 
                            spatial_size=spatial_size, hist_bins=hist_bins, 
                            orient=orient, pix_per_cell=pix_per_cell, 
                            cell_per_block=cell_per_block, 
                            hog_channel=hog_channel, spatial_feat=spatial_feat, 
                            hist_feat=hist_feat, hog_feat=hog_feat)                       

    window_img = draw_boxes(draw_img, hot_windows, color=(0, 0, 255), thick=6)                    
    images.append(window_img)
    titles.append('')
    print(time.time()-t1, 'seconds to process one image searching', len(windows), 'windows')
fig = plt.figure(figsize=(12,18), dpi=300)
visualize(fig, 5, 2, images, titles)
0.0 0.00392157
2.12813401222229 seconds to process one image searching 100 windows
0.0 0.00392157
0.8632955551147461 seconds to process one image searching 100 windows
0.0 0.00392157
0.7530012130737305 seconds to process one image searching 100 windows
0.0 0.00392157
0.7329466342926025 seconds to process one image searching 100 windows
0.0 0.00392157
0.7249252796173096 seconds to process one image searching 100 windows
0.0 0.00392157
0.7550077438354492 seconds to process one image searching 100 windows
In [7]:
#plt.imshow(window_img)
for i, img in enumerate(images):
    plt.imsave('test_images_results/test_bboxes'+str(i+1)+'.png',images[i])
In [8]:
# function for converting color spaces

def convert_color(img, conv='RGB2YCrCb'):
    if conv == 'RGB2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    if conv == 'BGR2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
    if conv == 'RGB2LUV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
In [9]:
# Create a heatmap on test images

out_images = []
out_maps = []
out_titles = []
out_boxes = []

ystart = 400
ystop = 656
scale = 1.5
#scale = 1

for img_src in example_images:
    img_boxes = []
    t = time.time()
    count = 0
    img = mpimg.imread(img_src)
    draw_img = np.copy(img)
    
    heatmap = np.zeros_like(img[:,:,0])
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]
    ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
    
    if scale != 1:
        imshape = ctrans_tosearch.shape
        ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))

    ch1 = ctrans_tosearch[:,:,0]
    ch2 = ctrans_tosearch[:,:,1]
    ch3 = ctrans_tosearch[:,:,2]

    nxblocks = (ch1.shape[1] // pix_per_cell) - 1
    nyblocks = (ch1.shape[0] // pix_per_cell) - 1
    nfeat_per_block = orient*cell_per_block**2
    window = 64
    nblocks_per_window = (window // pix_per_cell) - 1
    cells_per_step = 2
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step

    hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)

    for xb in range(nxsteps):
        for yb in range(nysteps):
            count += 1
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step

            #extract HOG for this patch
            hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
            hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
            hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()

            hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))

            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell

            #extract the image patch
            subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64, 64))

            #get color features
            spatial_features = bin_spatial(subimg, size=spatial_size)
            hist_features = color_hist(subimg, nbins=hist_bins)

            #scale features and make predictions
            test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)))
            #test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))            

            test_prediction = svc.predict(test_features)

            if test_prediction == 1:
                xbox_left = np.int(xleft*scale)
                ytop_draw = np.int(ytop*scale)   
                win_draw = np.int(window*scale)    
                cv2.rectangle(draw_img, (xbox_left, ytop_draw+ystart), (xbox_left+win_draw, ytop_draw+win_draw+ystart),(0,0,255))
                img_boxes.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw, ytop_draw+win_draw+ystart)))                     
                heatmap[ytop_draw+ystart:ytop_draw+win_draw+ystart, xbox_left:xbox_left+win_draw] += 1

    print(time.time()-t, 'seconds to run, total windows = ', count)

    out_images.append(draw_img)

    out_titles.append(img_src[-9:-4] + '_org_with_boxes.png')
    out_titles.append(img_src[-9:-4] + '_just_heatmap.png')
    

    out_images.append(heatmap)
    out_maps.append(heatmap)        
    out_boxes.append(heatmap)
                                                   
fig = plt.figure(figsize = (12,24))
visualize(fig, 8, 2, out_images, out_titles)
2.2688348293304443 seconds to run, total windows =  294
0.4882972240447998 seconds to run, total windows =  294
0.4842867851257324 seconds to run, total windows =  294
0.4722559452056885 seconds to run, total windows =  294
0.4602227210998535 seconds to run, total windows =  294
0.4742617607116699 seconds to run, total windows =  294
In [10]:
#plt.imshow(window_img)
for i, img in enumerate(out_images):
    plt.imsave('test_images_results/'+out_titles[i],out_images[i])
In [11]:
### Detect Vehicles

#dist_pickle = pickle.load( open("svc_pickle.p", "rb" ) )
#svc = dist_pickle["svc"]
#X_scaler = dist_pickle["scaler"]
#orient = dist_pickle["orient"]
#pix_per_cell = dist_pickle["pix_per_cell"]
#cell_per_block = dist_pickle["cell_per_block"]
#spatial_size = dist_pickle["spatial_size"]
#hist_bins = dist_pickle["hist_bins"]

#img = mpimg.imread('test_image.jpg')

# Define a single function that can extract features using hog sub-sampling and make predictions
#def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
def find_cars(img, scale):
  
    draw_img = np.copy(img)
    heatmap = np.zeros_like(img[:,:,0])
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]
    ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
    if scale != 1:
        imshape = ctrans_tosearch.shape
        ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
        
    ch1 = ctrans_tosearch[:,:,0]
    ch2 = ctrans_tosearch[:,:,1]
    ch3 = ctrans_tosearch[:,:,2]

    # Define blocks and steps as above
    nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
    nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1 
    nfeat_per_block = orient*cell_per_block**2
    
    # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
    window = 64
    #nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
    nblocks_per_window = (window // pix_per_cell) - 1
    cells_per_step = 2  # Instead of overlap, define how many cells to step
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step
    
    # Compute individual channel HOG features for the entire image
    hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
    
    for xb in range(nxsteps):
        for yb in range(nysteps):
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step
            # Extract HOG for this patch
            hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))

            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell

            # Extract the image patch
            subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
          
            # Get color features
            spatial_features = bin_spatial(subimg, size=spatial_size)
            hist_features = color_hist(subimg, nbins=hist_bins)

            # Scale features and make a prediction
            test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))    
            #test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))    
            test_prediction = svc.predict(test_features)
            
            if test_prediction == 1:
                xbox_left = np.int(xleft*scale)
                ytop_draw = np.int(ytop*scale)
                win_draw = np.int(window*scale)
                #cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6) 
                cv2.rectangle(draw_img, (xbox_left, ytop_draw+ystart), (xbox_left+win_draw, ytop_draw+win_draw+ystart),(0,0,255))
                img_boxes.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw, ytop_draw+win_draw+ystart)))                     
                heatmap[ytop_draw+ystart:ytop_draw+win_draw+ystart, xbox_left:xbox_left+win_draw] += 1

    return draw_img, heatmap
 
#ystart = 400
#ystop = 656
#scale = 1.5
    
#out_img = find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)

#plt.imshow(out_img)
In [12]:
def apply_threshold(heatmap, threshold):
    # Zero out pixels below the threshold
    heatmap[heatmap <= threshold] = 0
    # Return thresholded map
    return heatmap

def draw_labeled_bboxes(img, labels):
    # Iterate through all detected cars
    for car_number in range(1, labels[1]+1):
        # Find pixels with each car_number label value
        nonzero = (labels[0] == car_number).nonzero()
        # Identify x and y values of those pixels
        nonzeroy = np.array(nonzero[0])
        nonzerox = np.array(nonzero[1])
        # Define a bounding box based on min/max x and y
        bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
        # Draw the box on the image
        cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
    # Return the image
    return img
In [13]:
out_images = []
out_maps = []
ystart = 400
ystop = 656
scale = 1.5

for img_src in example_images:

    img = mpimg.imread(img_src)
    out_img, heat_map = find_cars(img, scale)
    
    threshold = 1
    heat_map = apply_threshold(heat_map, threshold)
    labels = label(heat_map)
    
    #draw bounding boxes on the image
    draw_img = draw_labeled_bboxes(np.copy(img), labels)
    out_images.append(draw_img)
    out_images.append(heat_map)
#    cv2.imwrite('/test_images_results/test1_bbox.jpg',draw_img)
#    cv2.imwrite('/test1_heatmap.jpg',heat_map)
    
fig = plt.figure(figsize = (12,24))
visualize(fig, 8, 2, out_images, out_titles)
In [14]:
def process_image(img):
    
    out_img, heat_map = find_cars(img, scale)
    threshold = 1
    heat_map = apply_threshold(heat_map, threshold)
    labels = label(heat_map)
    draw_img = draw_labeled_bboxes(np.copy(img), labels)
    return draw_img
In [15]:
test_output = 'test.mp4'
#clip = VideoFileClip("project_video.mp4")
clip = VideoFileClip("test_video.mp4")
test_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time test_clip.write_videofile(test_output, audio=False)
#test_clip.write_videofile(test_output, audio=False)
[MoviePy] >>>> Building video test.mp4
[MoviePy] Writing video test.mp4
 97%|███████████████████████████████████████████████████████████████████████████████▉  | 38/39 [00:15<00:00,  2.56it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: test.mp4 

Wall time: 16.5 s
In [16]:
HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(test_output))
Out[16]:
In [17]:
final_output = 'final.mp4'
clip = VideoFileClip("project_video.mp4")
final_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time final_clip.write_videofile(final_output, audio=False)
#test_clip.write_videofile(test_output, audio=False)
[MoviePy] >>>> Building video final.mp4
[MoviePy] Writing video final.mp4
100%|█████████████████████████████████████████████████████████████████████████████▉| 1260/1261 [08:21<00:00,  2.49it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: final.mp4 

Wall time: 8min 23s
In [18]:
HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(final_output))
Out[18]:
In [ ]: